Reservoir systems, such as petroleum reservoirs, typically contain fluids such as water and a mixture of hydrocarbons such as oil and gas. Different mechanisms can be utilized such as primary, secondary or tertiary recovery processes to produce the hydrocarbons from the reservoir.
In a primary recovery process, hydrocarbons are displaced from a reservoir due to the high natural differential pressure between the reservoir and the bottomhole pressure within a wellbore. The reservoir's energy and natural forces drive the hydrocarbons contained in the reservoir into the production well and up to the surface. Artificial lift systems, such as sucker rod pumps, electrical submersible pumps or gas-lift systems, are often implemented in the primary production stage to reduce the bottomhole pressure within the well. Such systems increase the differential pressure between the reservoir and the wellbore intake; thus, increasing hydrocarbon production. However, even with the use of such artificial lift systems only a small fraction of the original-oil-in-place (OOIP) is typically recovered in a primary recovery process. This is the case because the reservoir pressure and the differential pressure between the reservoir and the wellbore intake declines overtime due to production. For example, typically only about 10-20% of the OOIP can be produced before primary recovery reaches its limit—either when the reservoir pressure is too low that the production rates are not economical, or when the proportions of gas or water in the production stream are too high.
In order to increase the production life of the reservoir, secondary or tertiary recovery processes can be used. Typically in these processes, fluids such as water, gas, polymer, surfactant, or combination thereof, are injected into the reservoir to maintain reservoir pressure and drive the hydrocarbons to production wells. Secondary and tertiary recovery processes have already converted billions of barrels of proven oil resources to reserves, and typically produce an additional 10-50% of OOIP to that produced during primary recovery. The most commonly used secondary recovery process is waterflooding, which is commonly referred to as an improved oil recovery (IOR) process and involves the injection of water into the reservoir to displace or physically sweep the residual oil to adjacent production wells.
The success of secondary or tertiary recovery processes, such as waterflooding, depends on its ability to sweep remaining oil efficiently. Various prediction techniques and management methods have been developed to aid in evaluating and optimizing the performance behavior of these recovery processes. For example, such prediction techniques and management methods include reservoir surveillance, pattern balancing, sensitivity studies centered on finite difference simulation, or a combination thereof. Prediction techniques provide insight on the upside oil recovery potential by estimating the fraction of a reservoir that has or has not been swept by an injected fluid. Typically these estimates are based on net injected fluid volumes. Management methods include finding the optimum injection scheme and well controls to maximize the recovery of oil in the unswept portion of the reservoir.
Based on the prediction and optimization results, actions are typically taken to improve the sweep efficiency in both new and mature flooding processes. Such actions can include optimizing rate allocation, mechanical and chemical conformance control, infill drilling, well conversion, pattern realignment, or a combination thereof. For example, flooding fluid can be redistributed in a reservoir by manipulating the pressure field via individual well controls. For instance, if a high permeability streak is identified in an inverted five-spot pattern during a waterflood recovery process, the rate of the center injection well can be reduced such that water can be reallocated to other quadrants in the pattern. Reducing the flow rate of the injection well can also delay breakthrough and reduce field water cut. Thus, areal sweep efficiency can be improved by reallocating fluid flow rates of wells. However, identifying the appropriate well controls can be difficult because changing a given bottomhole pressure can affect the distribution of the flooding fluid from multiple injection wells. Consequently, this action may inadvertently reduce the daily production rate of other surrounding production wells in the pattern and ultimately lead to an overall poor sweep for a given time.
Various optimum rate control methods have been put forth for resolving the complex interactions of wells and determining optimum operating conditions. In general, these methods are systematic approaches for determining optimum rate allocation to improve sweep through a reservoir. For example, an optimum rate control method can be directed to maximizing the displacement efficiency at water breakthrough. To accomplish this, an optimization method might try to ensure simultaneous water arrival at production wells. In this case, injection wells might be restricted to operate at their maximum allowable injection rate or be fully shut, and an optimization algorithm could be used to determine the optimal switch time between the injection well operating extremes.
Another example of a rate control method includes using inflow control valves (ICVs) along a well to maintain constant flow rates during the recovery process until the flooding fluid arrives at the production well. Various heuristic algorithms can be utilized to reduce the impact of high-permeability streaks on recovery performance. For example, an optimal rate allocation can reduce the distribution of water-arrival times at various segments along the production well. This rate control method is referred to as a static optimization of a flooding process. An extension of this static rate control optimization method employs smart wells and allows dynamic control of the ICVs. In this case, wells are equipped with numerous ICVs along their profile and optimization can be analyzed under rate-constrained and bottomhole-pressure-constrained well conditions. Net present value (NPV) can then be maximized by changing the rate profile along the well segments throughout the optimization period.
While the above conventional optimum rate control management methods are able to enhance oil recovery, they tend to be time-consuming as they rely on finite difference field models that contain relatively high-resolution numerical grids. Simulation is therefore, generally not practical with a vast number of wells or with large reservoir models as simulation is encumbered by the level of detail within the model. Furthermore, these methods generally fail to account for inter-well connectivity while optimizing sweep—a key factor influencing the complex interactions between wells. Rather, heterogeneity is defined in conventional models typically as a contrast in reservoir properties and thus, does not appropriately model reservoir flow paths between wells.
Streamline-based optimum rate control methods have been developed to overcome the aforementioned problems of conventional methods. Streamline models solve for fluid pressures on a grid and construct streamlines to describe flow geometry between sources and sinks. Streamlines are constructed such that they are normal to the pressure field and can take any arbitrary shape as they are not constructed along a finite difference grid. Streamline simulation can be used to quickly evaluate how the geology of a subsurface reservoir will impact flow, even for large reservoir models. Furthermore, streamline simulation directly accounts for dynamic heterogeneity in reservoir models as individual streamlines represent fluid-front propogation at various times between injection and production wells.
Initially streamlines were solely used for flow visualization and calculation of static allocation factors; however, recent methods have combined formal optimization approaches with streamlines. For example, one streamline-based optimization method relies on dynamic well allocation factors, as opposed to conventional static allocation factors. In this method, allocation factors are used to calculate the efficiency of each injection well, and the injection rates are subsequently reallocated to improve recovery performance. Another streamline-based waterflood optimization method is directed to equalizing the arrival time of the waterflood front at all production wells within selected sub-regions. This method also analytically calculates sensitivities of water arrival times to well controls. Other previously developed optimum rate control methods include optimizing recovery processes under geological uncertainty and performing ensemble-based closed-loop optimizations. Additionally, certain methods have incorporated saturation normalization to account for different rock types and localization to alleviate the effect of spurious correlations.
Despite these efforts, previous conventional rate control methods and streamline-base optimization methods fail to ensure optimal sweep efficiency of a reservoir. Moreover, these previous methods fail to directly optimize sweep efficiency at any arbitrary time during the flood, independent of the flood history. Such incorrect or insufficient flooding design can lead to increased costs associated with cycling of injection fluid and poor sweep, especially as a flooding process matures.